Thermal comfort assessment of buildings

S P R I N G E R B R I E F S I N A P P L I E D S C I E N C E S A N D

TECHNOLOGY  POLIMI SPRINGER BRIEFS

Salvatore Carlucci

Thermal Comfort

Assessment of

Buildings

Salvatore Carlucci

Thermal Comfort

Assessment of Buildings

123

Salvatore Carlucci

Energy Department

Politecnico di Milano

Milan

Italy

ISSN 2282-2577 ISSN 2282-2585 (electronic)

ISBN 978-88-470-5237-6 ISBN 978-88-470-5238-3 (eBook)

DOI 10.1007/978-88-470-5238-3

Springer Milan Heidelberg New York Dordrecht London

Library of Congress Control Number: 2013930608

The Author(s) 2013

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Foreword

The wealth of research on thermal comfort has been partially taken and crystal￾lized into international standards, where thermal comfort is defined as: ‘‘that

condition of mind which expresses satisfaction with the thermal environment and

is assessed by subjective evaluation’’. A selection of subjective judgment scales

has been described, e.g., in ISO 10551. Those scales propose a set of answers to

questions as: ‘‘how do you feel at this precise moment?’’, or ‘‘please state how you

would prefer to be now’’, so they allow collecting information about the thermal

sensation and preference of a certain subject in a given place at a given time.

The data collected via these standardized surveys in the laboratory and in the

field have been interpreted, and meaningful correlations between the answers and

various physical variables have been derived, giving rise to what are generally

called comfort models, for example, the Fanger whole-body steady-state heat

balance model, the Pierce two-node model, the adaptive models and others. All

these models have as input the here-and-now questions and make here-and-now

predictions over the likely answers of a group of people in a certain environment.

But, when assessing comfort performances of an existing building or using a

certain comfort target interval as one of the objectives of a building design process,

one is generally interested in the overall performance. So one would attempt to

consider some adequate average over time (e.g., a season, a year, etc.) and space

(e.g., all occupied thermal zones of a building) of the here-and-now thermal comfort

values, be them gathered via direct interviews in a building or calculated via one of

the models. Disparate averaging algorithms have been proposed in the literature, and

some are presented in the standards and available for use in applications.

All this at least in theory; in everyday practice budget constraints and other

limitations have often led to using very simplified rules for assessment or design,

even not making explicit which model and assumptions are taken as a basis.

Averaging algorithms have been used often without an analysis of their implica￾tions on design choices, and very limited comparison between them has been

performed.

v

But in the last years, under the renewed effort toward low- and zero-energy

buildings, the issues of fine-tuning comfort and fully understanding its connection

with energy use have become increasingly important and urgent to address,

particularly so in warm climates and warm periods.

A number of European research projects (e.g., SCATs, Commoncense,

ThermCo, KeepCool) have explored these issues and added new data to the

comfort databases about occupied real buildings; conferences and networks such

as NCEUB, Palenc, and IEA SHC Task 40/ECBCS Annex 52 have been a fruitful

research cooperation and exchange opportunity for analyzing the implications on

comfort design; some of the new findings have found their way to the recent

update of the standards EN 15251, ISO 7730, and ASHRAE 55, and will influence

their further ongoing revision.

The research work of Dr. Carlucci presented in this book represents an

important contribution to these advancements and a fruit of his active engagement

in some of the mentioned projects and networks, in the framework of his partic￾ipation in the end-use Efficiency Research Group of Politecnico di Milano.

A careful review, comparison, and analysis of the large number of long-term

indexes proposed in the literature were highly needed and are now hence available.

Building on those, Carlucci proposes a new improved long-term general dis￾comfort index which aims at better matching the specific objectives of real world

assessment and design and to be applicable with the three main comfort models

presented in the standards. It also explicitly defines the operational use of the index

(e.g., how to define the length of the calculation period based on the actual climate

of the site) in order to overcome the present ambiguities that often undermine

the gnoseological and practical relevance of the results. Finally, he developed

three computer codes in the EnergyPlus Reference Language for calculating the

three versions of the new index and integrated them in the simulation environment

EnergyPlus in order to calculate the new index and to report it as a direct output of

the simulation.

Overall, a clear-cut methodology is here an essential tool to produce useful

results for real world applications.

Lorenzo Pagliano

Politecnico di Milano

vi Foreword

Acknowledgments

I wish to express my sincere gratitude to Prof. Edward Arens and Prof. Matheos

Santamouris for their valuable suggestions. I thank warmly Prof. Lorenzo Pagliano

for having supported me during the execution of this work and for having reviewed

it. I wish to express my gratitude also to Prof. Gabriele Masera for the inspiring

discussions that helped me in developing the topic. I am also grateful to my

colleagues and friends Dr. Paolo Zangheri, Eng. Marco Pietrobon, and Francesco

De Rosa who helped me in times of need. Infinite thanks to my family, whose

constant support helped me to complete this work. And of course, thanks to you,

Natascia, for your support and your even greater patience.

Salvatore Carlucci

vii

Contents

1 A Review of Long-Term Discomfort Indices ................. 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Indices Based on the Heat Balance

of the Human Body. . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.2 Indices Based on Physiological Strain . . . . . . . . . . . . . . 3

1.1.3 Indices Based on the Measurement of Physical

Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Indices for the Long-Term Evaluation of General

Thermal Discomfort. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.1 Indices Based on Comfort Models . . . . . . . . . . . . . . . . 6

1.2.2 Category-Dependent Indices . . . . . . . . . . . . . . . . . . . . . 6

1.2.3 Symmetric and Asymmetric Indices . . . . . . . . . . . . . . . 6

1.2.4 Indices Applicable Just to Summer or Extensible

also to Winter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.2.5 Discomfort Scales and Thresholds. . . . . . . . . . . . . . . . . 7

1.3 Description of the Long-Term Discomfort Indices . . . . . . . . . . . 7

1.3.1 Percentage Indices. . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.2 Cumulative Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.3.3 Risk Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.3.4 Averaging Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.5 Improvement Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2 Comparison of the Ranking Capabilities of the Long-Term

Discomfort Indices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2 The Adopted Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2.1 The Reference Building Model . . . . . . . . . . . . . . . . . . . 28

2.2.2 Physical Models Set in the Numerical Model . . . . . . . . . 29

2.2.3 Variations of the Technical Features of the Building

Envelope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

ix

2.2.4 Building Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.2.5 Comparison of the Building Variants. . . . . . . . . . . . . . . 33

2.3 Discussion of the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.3.1 Indices Unrepresentable on the Percentage Scale . . . . . . 37

2.3.2 Indices Representable on the Percentage Scale . . . . . . . . 40

2.3.3 Indices that Explicitly Make Use of Likelihood

of Dissatisfied . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.3.4 Inter-Comfort Model Correlation . . . . . . . . . . . . . . . . . 50

2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3 Gap Analysis of the Long-Term Discomfort Indices

and a Harmonized Calculation Framework. . . . . . . . . . . . . . . . . . 57

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.2.1 Uncertainty About the Calculation Period . . . . . . . . . . . 58

3.2.2 Rounded Boundary Temperatures of Comfort

Categories in EN 15251. . . . . . . . . . . . . . . . . . . . . . . . 61

3.2.3 Uncertainty About the Meteorological Input Variable

in ASHRAE 55. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.3 Gap Analysis of a Selection of Long-Term Discomfort Indices. . 62

3.3.1 Modification of the Calculation Period . . . . . . . . . . . . . 63

3.3.2 Duration of the Daily Occupation Schedule . . . . . . . . . . 67

3.3.3 Weak Definitions and Simplifications in Standards . . . . . 67

3.3.4 Fanger Boundary Temperatures of Comfort Categories

in EN 15251. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.4 Proposal for a Calculation Framework . . . . . . . . . . . . . . . . . . . 72

3.4.1 Thermal Zoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.4.2 Standard Input Parameters . . . . . . . . . . . . . . . . . . . . . . 74

3.4.3 Climatic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.4.4 Monitoring Campaign . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.5 Proposal for a Method for Identifying the Calculation Period . . . 75

3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4 The Long-Term Percentage of Dissatisfied . . . . . . . . . . . . . . . . . . 81

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.2 A New Long-Term Discomfort Index . . . . . . . . . . . . . . . . . . . 82

4.2.1 The Likelihood of Thermal Discomfort at a Specified

Time and Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.2.2 Averaging Over Different Zones of a Building . . . . . . . . 85

4.2.3 Proposal for a New Long-Term Thermal

Discomfort Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

x Contents

4.3 Likelihood of Thermal Discomfort Derived from Ashrae

Rp-884 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

4.4 Ranking Capability of Long-Term Percentage of Dissatisfied . . . 93

4.5 Integrating the Proposed Index in EnergyPlus . . . . . . . . . . . . . . 96

4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5 Conclusions and Future Developments . . . . . . . . . . . . . . . . . . . . . 101

Appendix A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Appendix B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Contents xi

Symbols and Abbreviations

Symbols

a Solar absorbance of a surface (dimensionless)

B Digit binary code: 0–1 (dimensionless)

c Solar factor (%)

h Heat transfer coefficient (W m-2 K-1

) or Hour (h)

I Global solar irradiance on a horizontal surface (W m-2

)

ma Average air velocity (m s-1

)

PMV Predicted mean vote (dimensionless)

PPD Predicted percentage of dissatisfied (%)

U Steady-state transmittance (W m-2 K-1

)

hop Operative temperature (C)

hdb Dry-bulb temperature (C)

hmr Mean radiant temperature (C)

hop Operative temperature (C)

hos Sol-Air temperature (C)

hres Dry-resultant temperature (C)

hrm Running mean of outside dry-bulb temperature (C)

wf Weighting factor (dimensionless)

Subscripts

actual Actual status

actual PMV Referred to PMV calculated in actual status

c Convective

C Cold period

comf Comfort

d Value averaged on a day

lower limit Lower limit of comfort range

OC Overcooling

OH Overheating

out Outdoor

xiii

PMV limit Referred to PMV limits

i General recursive index

in Indoor

r Radiative

t Recursive index for time

upper limit Upper limit of a comfort range

W Warm period

Y Year

z Recursive index for zones

Z Number of zones in a multi-zone building

xiv Symbols and Abbreviations

Acronyms

ANSI American National Standards Institute

ASHRAE American Society of Heating, Refrigerating and Air Conditioning

Engineers

CEN European Committee for Standardization

CIBSE Chartered Institution of Building Services Engineers

Dh Degree-hours

DhC Degree-hour criterion

US DOE Unites States Department of Energy

DSY Design Summer Year

ECBCS Energy Conservation in Buildings and Community Systems

EMS Energy Management System

EN European Standards

ERL EnergyPlus Runtime Language

EU European Union

HVAC Heating, Ventilation and Air Conditioning

IEA International Energy Agency

ISO International Organization for Standardization

IWEC International Weather for Energy Calculations

LPD Long-term Percentage of Dissatisfied

NaOR Nicol et al.,’s Overheating Risk

NREL National Energy Renewable Laboratory

PMV Predicted Mean Vote

POR Percentage Outside (comfort) Range

PPD Predicted Percentage of Dissatisfied

PPDwC PPD-weighted criterion

RHOR Robinson’s and Haldi’s Overheating risk

SCATs EU Project Smart Controls and Thermal Comfort

SHC Solar Heating and Cooling Program

SIA Swiss Society of Engineers and Architects

Sum_PPD Accumulated PPD

TMY Typical Meteorological Year

xv

TRY Typical reference Year

TRNSYS Transient system simulation program

USA United States of America

WYEC Weather Year for Energy Calculations

xvi Acronyms

Introduction

The specification of indoor thermal comfort requirements that a building must

provide is a prerequisite for its design, and reliable explicit methods for the

assessment of its long-term comfort performances are, therefore, necessary.

Several metrics for assessing human thermal response to climatic conditions or

stresses have been proposed in the scientific literature over the last decades, and a

number of authors have used, and still use, terms such as discomfort index, stress

index, or heat index to identify the analytical models that describe human thermal

perception of the thermal environment to which an individual or a group of people

is exposed. More recently, a new type of discomfort index has been proposed in the

scientific literature, in standards and guidelines, specifically for briefly describing

long-term thermal comfort conditions in buildings and for predicting uncomfort￾able phenomena, in particular summer overheating. Most of these new indices

summarize the thermal performance of a building into a single value.

These indices may be useful tools for the operational assessment of the thermal

comfort performance of an existing building or for guiding the optimization phase

of the design of a building envelope and its thermal plant systems and control

strategies. In particular, for zero energy—mainly passive—buildings, the possi￾bility to discriminate and rank building variants is not satisfactorily feasible by

comparing the energy consumption (ideally the best variants will all have energy

consumption values grouped in a small interval around zero). We argue here that

the ranking of these variants may be explicitly based on maximizing thermal

comfort performances of the envelope, passive systems, and their control

strategies. This also coincides with minimizing energy need (and hence energy

consumption of active systems whether present) for achieving comfort design

values, but is more flexible.

In Chap. 1, a hopefully exhaustive review of the existing indices for the long-term

evaluation of thermal comfort conditions in a building and for thermal risk

assessment is presented (i) since some of the them are based on thermal comfort

models, while others derive from rules of thumb, (ii) since they are considerably

different in their structure and significance and (iii) since a systematic collection of

those is missing.

xvii